Eliminate use of template in exporter #5163
GitHub Actions / Test Results
failed
Nov 20, 2023 in 0s
54 fail, 2 792 skipped, 8 342 pass in 54m 33s
Annotations
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__ops_aten__softmax_half_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
Failed: Unexpected success
Unexpected success
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:355: in aten_all_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
1 out of 9 runs failed: test_output_match_opinfo__ops_aten_index_Tensor_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 14s]
Raw output
EOFError
EOFError
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:534: in _capture_graph_and_evaluate_torch_script_evaluator
return _safe_ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:339: in _safe_ort_session_run
return_dict = manager.dict()
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/managers.py:723: in temp
token, exp = self._create(typeid, *args, **kwds)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/managers.py:606: in _create
conn = self._Client(self._address, authkey=self._authkey)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:508: in Client
answer_challenge(c, authkey)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:752: in answer_challenge
message = connection.recv_bytes(256) # reject large message
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:216: in recv_bytes
buf = self._recv_bytes(maxlength)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:414: in _recv_bytes
buf = self._recv(4)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:383: in _recv
raise EOFError
E EOFError
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:534: in _capture_graph_and_evaluate_torch_script_evaluator
return _safe_ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:338: in _safe_ort_session_run
manager = multiprocessing.Manager()
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/context.py:57: in Manager
m.start()
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/managers.py:566: in start
self._address = reader.recv()
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:250: in recv
buf = self._recv_bytes()
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:414: in _recv_bytes
buf = self._recv(4)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/multiprocessing/connection.py:383: in _recv
raise EOFError
E EOFError
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript\values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript\evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript\function_libs\torch_lib\ops\core.py:478: in aten_any_dim
dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript\onnx_opset\_impl\opset14.py:909: in Reshape
return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript\values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript\evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript\evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript\evaluator.py:471: in _call_ort
model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript\evaluator.py:411: in _prepare_model_and_inputs_for_eager
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:411: in <listcomp>
args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript\evaluator.py:377: in _onnxscript_to_numpy_value
raise TypeError(
E TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox\test_torch_nightly\lib\site-packages\onnx\checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addcdiv_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 25 (4.0%)
Greatest absolute difference: 0.0001220703125 at index (0, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.002079010009765625 at index (0, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 2 / 25 (8.0%)
Greatest absolute difference: 0.001953125 at index (1, 1) (up to 1e-05 allowed)
Greatest relative difference: 0.0024089813232421875 at index (2, 4) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 25 (4.0%)
Greatest absolute difference: 0.00018310546875 at index (2, 4) (up to 1e-05 allowed)
Greatest relative difference: 0.00147247314453125 at index (2, 4) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 4 / 25 (16.0%)
Greatest absolute difference: 0.00341796875 at index (1, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.0059967041015625 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 16 / 125 (12.8%)
Greatest absolute difference: 0.00390625 at index (4, 0, 4) (up to 1e-05 allowed)
Greatest relative difference: 0.01145172119140625 at index (3, 1, 0) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 17 / 125 (13.6%)
Greatest absolute difference: 0.005859375 at index (2, 4, 3) (up to 1e-05 allowed)
Greatest relative difference: 0.03094482421875 at index (2, 2, 1) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 25 (4.0%)
E Greatest absolute difference: 0.0001220703125 at index (0, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.002079010009765625 at index (0, 2) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 25 (8.0%)
E Greatest absolute difference: 0.001953125 at index (1, 1) (up to 1e-05 allowed)
E Greatest relative difference: 0.0024089813232421875 at index (2, 4) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 25 (4.0%)
E Greatest absolute difference: 0.00018310546875 at index (2, 4) (up to 1e-05 allowed)
E Greatest relative difference: 0.00147247314453125 at index (2, 4) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 4 / 25 (16.0%)
E Greatest absolute difference: 0.00341796875 at index (1, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.0059967041015625 at index (1, 2) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 16 / 125 (12.8%)
E Greatest absolute difference: 0.00390625 at index (4, 0, 4) (up to 1e-05 allowed)
E Greatest relative difference: 0.01145172119140625 at index (3, 1, 0) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 17 / 125 (13.6%)
E Greatest absolute difference: 0.005859375 at index (2, 4, 3) (up to 1e-05 allowed)
E Greatest relative difference: 0.03094482421875 at index (2, 2, 1) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__atan2_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 500 (0.2%)
Greatest absolute difference: 0.001953125 at index (7, 3, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.001094818115234375 at index (7, 3, 0) (up to 0.001 allowed)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 500 (0.2%)
E Greatest absolute difference: 0.001953125 at index (7, 3, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.001094818115234375 at index (7, 3, 0) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 6s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 43 / 50 (86.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: 1.0 at index (7,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript\tests\function_libs\torch_lib\ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 43 / 50 (86.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: 1.0 at index (7,)
Loading